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The accuracy of using administrative healthcare data to identify epilepsy cases: A systematic review of validation studies.
Mbizvo, Gashirai K; Bennett, Kyle H; Schnier, Christian; Simpson, Colin R; Duncan, Susan E; Chin, Richard F M.
Afiliação
  • Mbizvo GK; Muir Maxwell Epilepsy Centre, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
  • Bennett KH; Muir Maxwell Epilepsy Centre, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
  • Schnier C; Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
  • Simpson CR; Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
  • Duncan SE; School of Health, Faculty of Health, Victoria University of Wellington, Wellington, NZ.
  • Chin RFM; Muir Maxwell Epilepsy Centre, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
Epilepsia ; 61(7): 1319-1335, 2020 07.
Article em En | MEDLINE | ID: mdl-32474909
ABSTRACT
Our objective was to undertake a systematic review ascertaining the accuracy of using administrative healthcare data to identify epilepsy cases. We searched MEDLINE and Embase from 01/01/1975 to 03/07/2018 for studies evaluating the diagnostic accuracy of routinely collected healthcare data in identifying epilepsy cases. Any disease coding system in use since the International Classification of Diseases, Ninth Revision (ICD-9) was permissible. Two authors independently screened studies, extracted data, and quality-assessed studies. We assessed positive predictive value (PPV), sensitivity, negative predictive value (NPV), and specificity. The primary analysis was a narrative synthesis of review findings. Thirty studies were included, published between 1989 and 2018. Risks of bias were low, high, and unclear in 4, 14, and 12 studies, respectively. Coding systems included ICD-9, ICD-10, and Read Codes, with or without antiepileptic drugs (AEDs). PPVs included ranges of 5.2%-100% (Canada), 32.7%-96.0% (USA), 47.0%-100% (UK), and 37.0%-88.0% (Norway). Sensitivities included ranges of 22.2%-99.7% (Canada), 12.2%-97.3% (USA), and 79.0%-94.0% (UK). Nineteen studies contained at least one algorithm with a PPV >80%. Sixteen studies contained at least one algorithm with a sensitivity >80%. PPV was highest in algorithms consisting of disease codes (ICD-10 G40-41, ICD-9 345) in combination with one or more AEDs. The addition of symptom codes to this (ICD-10 R56; ICD-9 780.3, 780.39) lowered PPV. Sensitivity was highest in algorithms consisting of symptom codes with one or more AEDs. Although using AEDs alone achieved high sensitivities, the associated PPVs were low. Most NPVs and specificities were >90%. We conclude that it is reasonable to use administrative data to identify people with epilepsy (PWE) in epidemiological research. Studies prioritizing high PPVs should focus on combining disease codes with AEDs. Studies prioritizing high sensitivities should focus on combining symptom codes with AEDs. We caution against the use of AEDs alone to identify PWE.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Coleta de Dados / Bases de Dados Factuais / Atenção à Saúde / Epilepsia / Estudos de Validação como Assunto Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Epilepsia Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Coleta de Dados / Bases de Dados Factuais / Atenção à Saúde / Epilepsia / Estudos de Validação como Assunto Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Epilepsia Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido